Abstract
China has made many achievements in health care industry since the establishment of hierarchical medical treatment system, but also exposed a lot of problems, such as the problem of “easy to upward-referral, hard to downward-referral”, which occurred in the implementation process of two-way referral. Recently, the appearance of “hard to downward-referral” have gotten more and more attention from scholars. The key step in solving the problem of “downward-referral” is to classify hospitals at different levels evaluated by the professional doctors. This paper proposes a framework to solve the problem where evaluation values of the hospital are given as q-rung orthopair fuzzy uncertain linguistic variables (q-ROFULVs). Considering Schweizer-Sklar t-conorm and t-norm (SSTT) can make the information aggregation process more flexible, and Hamy Mean operator can consider the correlation between hospital indicators. In order to take full advantage of these two kinds of operators, firstly, we extend SSTT to q-ROFULVs and define Schweizer-Sklar operational rules of q-ROFULVs. Secondly, we combine the Hamy Mean operator with Schweizer-Sklar t-conorm and t-norm, and propose the q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar Hamy mean aggregation operators and q-Rung orthopair fuzzy uncertain linguistic Schweizer-Sklar weighted Hamy Mean respectively. Afterwards, q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar dual Hamy mean operator and its weighted form are also developed. Then, we have studied in detail some of the ideal properties of these operators. Furthermore, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, we apply the new approach to solve the problem about the downward conversion in the hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system.
Highlights
The hierarchical medical treatment system has been carried out for a long time in China, ‘‘diagnosis and treatment of grassroots first diagnosis, two-way referral, partition of acute and chronic diseases and upper and lower linkage’’.The associate editor coordinating the review of this manuscript and approving it for publication was Chi-Tsun Cheng .due to the large population base, medical resources are still limited, and high-quality medical resources are concentrated in large hospitals, resulting in overcrowding in large hospitals and waste of resources in small hospitals
(4) From the point view of aggregation operators, intuitionistic uncertain linguistic weighted Bonferroni mean (IULWBM) [28] can consider the interrelationship of the attributes, they only capture the interrelationship between two attributes, whereas the q-ROFULSSWHM and q-ROFULSSWDHM operators proposed in this paper can capture the interrelationships among the multi-input attributes according to different parameters k
The hierarchical medical treatment system is an efficient way to integrate all levels of medical service system resources and release the pressure of large hospitals in China, but there has always been a problem of difficulty in referrals
Summary
The hierarchical medical treatment system has been carried out for a long time in China, ‘‘diagnosis and treatment of grassroots first diagnosis, two-way referral, partition of acute and chronic diseases and upper and lower linkage’’. Selecting the best referral hospital for patients from many hospitals through the medical collaborative information system platform is a key step It has become an important research issue in medical management selecting referral hospital which often involves multiple criteria and experts, so it can be described as a multi-attribute group decision making (MAGDM) problem. Uncertain linguistic variables (ULVs) can more express fuzzy information than LVs. ULVs should be combined with q-ROFSs to propose new tools to describe decision makers’ evaluation values. For instance: (1) We need to exactly express fuzzy information, and q-Rung orthopair fuzzy uncertain linguistic variables (q-ROFULVs) can depict doctors’ evaluations for hospitals with respect to the indicator; (2) We need to consider correlations among evaluating indicators, and the Hamy Mean operator can be utilized to solve this problem; and (3) We need to increasing flexibility by taking into account the preferences of different decision makers.
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